Few therapies have made the successful transition from bench to bedside for spinal cord injury (SCI), suggesting a need to better understand drug trials for this complicated disease at the preclinical level. Due to the overwhelming volume of data that exists for preclinical trials, researchers often have substantial difficulty analyzing, visualizing, and interpreting the efficacy of the drugs being tested, and often times results are not replicable across multiple studies. We have employed a novel approach to address this problem by using multivariate statistical analyses combined with topological data analysis (TDA) methods to more accurately assess the syndromic efficacy of preclinical therapeutics. As proof-of-concept we analyzed 5 blinded, placebo controlled preclinical drug trials in graded cervical SCI in rats: minocycline, methylprednisolone, ciclopirox, DMSO, and a soluble TNFR1 dose-response. Multivariate tools included principal components analysis (PCA), discriminant function analysis (DFA), and multivariate analysis of variance (MANOVA), combined with a Fisher’s Exact test. Data were further analyzed using Iris software (Ayasdi, Inc), which employs TDA methods to identify the network topology of the full set of variables simultaneously. TDA resulted in a clear distinction of separate geometric flares based on the graded injuries in the dataset (shams, hemisections, mild IH contusions, moderate NYU contusions), with clear separation of each drug trial within their respective flares. Consensus across multiple types of analyses revealed that the only successful drug trial to reliably improve forelimb function following cervical SCI was the sTNFR1 dose-response and timing trials, which had a significant impact on recovery of function related to restoration of forepaw symmetry observed across multiple types of analyses and multiple endpoints. These findings suggest that syndromic TDA can be fruitfully applied to a wide array of raw preclinical trials data to rapidly detect robust effects of experimental drugs. This approach has potential to expedite identification of candidate therapies that could be considered for clinical trials.